
# importing the necessary libraries for analysis and visualizations
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import math
from datetime import datetime
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
from sklearn.preprocessing import MinMaxScaler
import copy

import warnings
warnings.filterwarnings("ignore")


# reading the data to a dataframe
df = pd.read_csv('all_ticks_wide.csv')

# converting to pd datetime
df['timestamp'] = pd.to_datetime(df['timestamp'])

# getting year, month, day as features
df['year'] = df['timestamp'].dt.year
df['month'] = df['timestamp'].dt.month
df['day'] = df['timestamp'].dt.day

df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50012 entries, 0 to 50011
Data columns (total 64 columns):
 #   Column     Non-Null Count  Dtype              
---  ------     --------------  -----              
 0   timestamp  50012 non-null  datetime64[ns, UTC]
 1   AEFES      48131 non-null  float64            
 2   AKBNK      49209 non-null  float64            
 3   AKSA       48594 non-null  float64            
 4   AKSEN      48171 non-null  float64            
 5   ALARK      48335 non-null  float64            
 6   ALBRK      46862 non-null  float64            
 7   ANACM      48165 non-null  float64            
 8   ARCLK      49045 non-null  float64            
 9   ASELS      48803 non-null  float64            
 10  ASUZU      48433 non-null  float64            
 11  AYGAZ      48119 non-null  float64            
 12  BAGFS      48650 non-null  float64            
 13  BANVT      47951 non-null  float64            
 14  BRISA      48937 non-null  float64            
 15  CCOLA      48749 non-null  float64            
 16  CEMAS      46394 non-null  float64            
 17  ECILC      48492 non-null  float64            
 18  EREGL      49173 non-null  float64            
 19  FROTO      48995 non-null  float64            
 20  GARAN      49308 non-null  float64            
 21  GOODY      48961 non-null  float64            
 22  GUBRF      49057 non-null  float64            
 23  HALKB      49071 non-null  float64            
 24  ICBCT      44336 non-null  float64            
 25  ISCTR      49221 non-null  float64            
 26  ISDMR      12227 non-null  float64            
 27  ISFIN      42877 non-null  float64            
 28  ISYAT      43184 non-null  float64            
 29  KAREL      46032 non-null  float64            
 30  KARSN      48527 non-null  float64            
 31  KCHOL      49093 non-null  float64            
 32  KRDMB      47532 non-null  float64            
 33  KRDMD      49161 non-null  float64            
 34  MGROS      48903 non-null  float64            
 35  OTKAR      48785 non-null  float64            
 36  PARSN      45325 non-null  float64            
 37  PETKM      49184 non-null  float64            
 38  PGSUS      45221 non-null  float64            
 39  PRKME      48466 non-null  float64            
 40  SAHOL      49095 non-null  float64            
 41  SASA       47633 non-null  float64            
 42  SISE       49090 non-null  float64            
 43  SKBNK      47270 non-null  float64            
 44  SODA       48276 non-null  float64            
 45  TCELL      49143 non-null  float64            
 46  THYAO      49282 non-null  float64            
 47  TKFEN      48930 non-null  float64            
 48  TOASO      48946 non-null  float64            
 49  TRKCM      48886 non-null  float64            
 50  TSKB       48384 non-null  float64            
 51  TTKOM      49077 non-null  float64            
 52  TUKAS      45929 non-null  float64            
 53  TUPRS      49143 non-null  float64            
 54  USAK       47659 non-null  float64            
 55  VAKBN      49212 non-null  float64            
 56  VESTL      48781 non-null  float64            
 57  YATAS      46055 non-null  float64            
 58  YKBNK      49225 non-null  float64            
 59  YUNSA      45528 non-null  float64            
 60  ZOREN      48807 non-null  float64            
 61  year       50012 non-null  int64              
 62  month      50012 non-null  int64              
 63  day        50012 non-null  int64              
dtypes: datetime64[ns, UTC](1), float64(60), int64(3)
memory usage: 24.4 MB


# function to fill null values
# it takes the 
def fill_null_values_with_average(df, timestamp):
    filled_df = df.copy()
    other_columns = list(filled_df.columns.difference([timestamp]))
    for column in other_columns:
        # creating a mask for NaN values in the column
        null_mask = filled_df[column].isnull()
        # finding the first non-null value before each NaN
        before = filled_df[column].where(~null_mask).ffill()
        # finding the first non-null value after each NaN
        after = filled_df[column].where(~null_mask[::-1]).bfill()[::-1]
        # taking the average of them
        average = (before + after) / 2
        # filling the nan
        filled_df[column].fillna(average, inplace=True)
    return filled_df

# filling the nan
filled_df = fill_null_values_with_average(df, 'timestamp')
filled_df


# info about number of outliers
filled_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50012 entries, 0 to 50011
Data columns (total 64 columns):
 #   Column     Non-Null Count  Dtype              
---  ------     --------------  -----              
 0   timestamp  50012 non-null  datetime64[ns, UTC]
 1   AEFES      50012 non-null  float64            
 2   AKBNK      50012 non-null  float64            
 3   AKSA       50012 non-null  float64            
 4   AKSEN      50012 non-null  float64            
 5   ALARK      50012 non-null  float64            
 6   ALBRK      50012 non-null  float64            
 7   ANACM      50012 non-null  float64            
 8   ARCLK      50012 non-null  float64            
 9   ASELS      49982 non-null  float64            
 10  ASUZU      50012 non-null  float64            
 11  AYGAZ      50012 non-null  float64            
 12  BAGFS      50012 non-null  float64            
 13  BANVT      50012 non-null  float64            
 14  BRISA      50012 non-null  float64            
 15  CCOLA      50012 non-null  float64            
 16  CEMAS      50012 non-null  float64            
 17  ECILC      50012 non-null  float64            
 18  EREGL      50012 non-null  float64            
 19  FROTO      50012 non-null  float64            
 20  GARAN      50012 non-null  float64            
 21  GOODY      50012 non-null  float64            
 22  GUBRF      50012 non-null  float64            
 23  HALKB      50012 non-null  float64            
 24  ICBCT      50012 non-null  float64            
 25  ISCTR      50012 non-null  float64            
 26  ISDMR      26057 non-null  float64            
 27  ISFIN      50012 non-null  float64            
 28  ISYAT      50012 non-null  float64            
 29  KAREL      49980 non-null  float64            
 30  KARSN      49980 non-null  float64            
 31  KCHOL      50012 non-null  float64            
 32  KRDMB      50012 non-null  float64            
 33  KRDMD      50012 non-null  float64            
 34  MGROS      50012 non-null  float64            
 35  OTKAR      50012 non-null  float64            
 36  PARSN      50012 non-null  float64            
 37  PETKM      50012 non-null  float64            
 38  PGSUS      46015 non-null  float64            
 39  PRKME      50012 non-null  float64            
 40  SAHOL      50012 non-null  float64            
 41  SASA       49980 non-null  float64            
 42  SISE       49980 non-null  float64            
 43  SKBNK      50012 non-null  float64            
 44  SODA       50012 non-null  float64            
 45  TCELL      50012 non-null  float64            
 46  THYAO      50012 non-null  float64            
 47  TKFEN      50012 non-null  float64            
 48  TOASO      50012 non-null  float64            
 49  TRKCM      49980 non-null  float64            
 50  TSKB       50012 non-null  float64            
 51  TTKOM      50012 non-null  float64            
 52  TUKAS      50012 non-null  float64            
 53  TUPRS      50012 non-null  float64            
 54  USAK       50012 non-null  float64            
 55  VAKBN      50012 non-null  float64            
 56  VESTL      50012 non-null  float64            
 57  YATAS      50012 non-null  float64            
 58  YKBNK      50012 non-null  float64            
 59  YUNSA      50012 non-null  float64            
 60  ZOREN      49980 non-null  float64            
 61  year       50012 non-null  int64              
 62  month      50012 non-null  int64              
 63  day        50012 non-null  int64              
dtypes: datetime64[ns, UTC](1), float64(60), int64(3)
memory usage: 24.4 MB


# copy df
df = filled_df.copy()
# start date
start = pd.to_datetime("2017-01-15 09:30:00+00:00")
# end data
end = pd.to_datetime("2019-01-15 09:30:00+00:00")
# filtering with time
ndf = df[(df['timestamp'] >= start) & (df['timestamp'] <= end)]
# filtering the necessary columns 
df = ndf[['timestamp', 'year', 'month', 'AKBNK', 'VAKBN', 'ARCLK', 'TUPRS', 'TCELL', 'THYAO']].copy()

# years and months as array
years = [2017, 2018, 2019]
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

# the selected stocks
indexes_selected = ['AKBNK', 'VAKBN', 'ARCLK', 'TUPRS', 'TCELL', 'THYAO']

outliers_akbnk = []
outliers_vakbn = []
outliers_arclk = []
outliers_tuprs = []
outliers_tcell = []
outliers_thyao = []

def get_list(index):
    if index == "AKBNK":
        return outliers_akbnk
    elif index == "VAKBN":
        return outliers_vakbn
    elif index == "ARCLK":
        return outliers_arclk
    elif index == "TUPRS":
        return outliers_tuprs
    elif index == "TCELL":
        return outliers_tcell
    else:
        return outliers_thyao

# given a stock in a specific month, constructs the 
# control charts for that month
def plot_control_chart(selected):
    for index in indexes_selected:
        # getting the prices for the stock
        prices = selected[index].to_list()
        # timestamps
        time = selected['timestamp'].to_list()
        # mean
        mu = np.mean(prices)
        # standard deviation
        sigma = np.std(prices)
        # UCL and LCL
        UCL = mu + 3 * sigma
        LCL = mu - 3 * sigma
        
        # create a list to store the outliers
        outliers = []
        # plot Mean, UCL, LCL
        plt.figure(figsize=(30, 15))
        plt.plot(time, prices, marker='o', label='Data')
        plt.axhline(mu, color='r', linestyle='--', label='Mean')
        plt.axhline(UCL, color='g', linestyle='--', label='UCL (3-sigma)')
        plt.axhline(LCL, color='g', linestyle='--', label='LCL (3-sigma)')
        
        # plot outliers as red dots
        for i, price in enumerate(prices):
            if price > UCL or price < LCL:
                plt.scatter(time[i], price, c='red', marker='o', s=200)
                outliers.append((time[i], price))
        # plot dates, x axis, y axis, title
        date_format = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
        plt.gca().xaxis.set_major_formatter(date_format)
        plt.gca().xaxis.set_major_locator(mdates.HourLocator(interval=12))
        plt.xlabel('Time', fontsize=26)
        plt.ylabel('Price', fontsize=26)
        plt.title(f'Control Chart for {index} with 3-sigma Limits\nYear: {selected["year"].iloc[0]}, Month: {selected["month"].iloc[0]}', fontsize=32, fontweight='bold')
        plt.legend(fontsize=25)
        plt.grid(True)
        plt.xticks(rotation=45, fontsize=18)
        plt.yticks(fontsize=20)
        plt.show()
        # print outliers
        if outliers:
            print(f'Outliers for {index}:')
            l = get_list(index)
            for outlier in outliers:
                l.append((outlier[0],outlier[1]))
                print(f'Timestamp: {outlier[0]}, Price: {outlier[1]}')

# group by year, month
grouped = df.groupby(['year', 'month'])

# apply the plot_control_chart function to each group
grouped.apply(plot_control_chart)

Outliers for AKBNK:
Timestamp: 2017-01-31 11:30:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 14:15:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 14:30:00+00:00, Price: 6.7803

Outliers for AKBNK:
Timestamp: 2017-02-01 08:15:00+00:00, Price: 6.6681
Timestamp: 2017-02-01 08:30:00+00:00, Price: 6.6521

Outliers for VAKBN:
Timestamp: 2017-02-01 06:45:00+00:00, Price: 4.8252
Timestamp: 2017-02-01 07:45:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:15:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:30:00+00:00, Price: 4.8055

Outliers for THYAO:
Timestamp: 2017-02-28 12:15:00+00:00, Price: 5.46
Timestamp: 2017-02-28 12:30:00+00:00, Price: 5.45
Timestamp: 2017-02-28 12:45:00+00:00, Price: 5.42
Timestamp: 2017-02-28 13:00:00+00:00, Price: 5.42
Timestamp: 2017-02-28 13:15:00+00:00, Price: 5.44
Timestamp: 2017-02-28 13:30:00+00:00, Price: 5.46
Timestamp: 2017-02-28 13:45:00+00:00, Price: 5.45
Timestamp: 2017-02-28 14:00:00+00:00, Price: 5.46
Timestamp: 2017-02-28 14:15:00+00:00, Price: 5.46
Timestamp: 2017-02-28 14:30:00+00:00, Price: 5.45
Timestamp: 2017-02-28 14:45:00+00:00, Price: 5.45
Timestamp: 2017-02-28 15:00:00+00:00, Price: 5.46

Outliers for AKBNK:
Timestamp: 2017-03-06 12:45:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:00:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:15:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:30:00+00:00, Price: 7.4375
Timestamp: 2017-03-06 13:45:00+00:00, Price: 7.4294
Timestamp: 2017-03-06 14:00:00+00:00, Price: 7.4294
Timestamp: 2017-03-07 06:45:00+00:00, Price: 7.4535
Timestamp: 2017-03-07 07:00:00+00:00, Price: 7.4294
Timestamp: 2017-03-07 07:15:00+00:00, Price: 7.4615
Timestamp: 2017-03-07 07:30:00+00:00, Price: 7.4375
Timestamp: 2017-03-07 07:45:00+00:00, Price: 7.4455

Outliers for VAKBN:
Timestamp: 2017-03-06 12:15:00+00:00, Price: 5.4906
Timestamp: 2017-03-06 12:30:00+00:00, Price: 5.5005
Timestamp: 2017-03-06 12:45:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:00:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:15:00+00:00, Price: 5.52
Timestamp: 2017-03-06 13:30:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 13:45:00+00:00, Price: 5.5102
Timestamp: 2017-03-06 15:00:00+00:00, Price: 5.4906
Timestamp: 2017-03-07 06:45:00+00:00, Price: 5.4906
Timestamp: 2017-03-07 07:15:00+00:00, Price: 5.5005

Outliers for TUPRS:
Timestamp: 2017-04-28 14:30:00+00:00, Price: 71.0298
Timestamp: 2017-04-28 14:45:00+00:00, Price: 71.0298
Timestamp: 2017-04-28 15:00:00+00:00, Price: 71.0298

Outliers for ARCLK:
Timestamp: 2017-06-01 06:45:00+00:00, Price: 23.7059
Timestamp: 2017-06-01 07:00:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 07:15:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 07:30:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 07:45:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 08:00:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 08:15:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 08:30:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 08:45:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 09:00:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 09:15:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 09:30:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 09:45:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 10:45:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 11:00:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 11:15:00+00:00, Price: 23.6866
Timestamp: 2017-06-01 11:30:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 11:45:00+00:00, Price: 23.7638
Timestamp: 2017-06-01 12:00:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 12:15:00+00:00, Price: 23.8024
Timestamp: 2017-06-01 12:30:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 12:45:00+00:00, Price: 23.7831
Timestamp: 2017-06-01 13:00:00+00:00, Price: 23.7445
Timestamp: 2017-06-01 13:15:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 13:30:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 13:45:00+00:00, Price: 23.7059
Timestamp: 2017-06-01 14:00:00+00:00, Price: 23.648
Timestamp: 2017-06-01 14:15:00+00:00, Price: 23.6673
Timestamp: 2017-06-01 14:30:00+00:00, Price: 23.7252
Timestamp: 2017-06-01 14:45:00+00:00, Price: 23.841

Outliers for TUPRS:
Timestamp: 2017-08-01 09:00:00+00:00, Price: 84.2834
Timestamp: 2017-08-01 09:15:00+00:00, Price: 84.7596
Timestamp: 2017-08-01 09:30:00+00:00, Price: 84.7596
Timestamp: 2017-08-01 09:45:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 10:45:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 11:00:00+00:00, Price: 84.5215
Timestamp: 2017-08-01 12:15:00+00:00, Price: 84.6008
Timestamp: 2017-08-01 12:30:00+00:00, Price: 84.4421
Timestamp: 2017-08-01 12:45:00+00:00, Price: 84.204
Timestamp: 2017-08-01 13:00:00+00:00, Price: 84.3627
Timestamp: 2017-08-01 13:15:00+00:00, Price: 84.4421
Timestamp: 2017-08-01 13:30:00+00:00, Price: 84.2834
Timestamp: 2017-08-01 13:45:00+00:00, Price: 84.204
Timestamp: 2017-08-01 14:00:00+00:00, Price: 84.0453
Timestamp: 2017-08-01 14:15:00+00:00, Price: 84.0453
Timestamp: 2017-08-01 14:30:00+00:00, Price: 84.1246
Timestamp: 2017-08-02 06:45:00+00:00, Price: 84.7596

Outliers for AKBNK:
Timestamp: 2017-10-09 07:00:00+00:00, Price: 7.3946
Timestamp: 2017-10-09 07:30:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 07:45:00+00:00, Price: 7.4439
Timestamp: 2017-10-09 08:00:00+00:00, Price: 7.4357
Timestamp: 2017-10-09 08:15:00+00:00, Price: 7.4192
Timestamp: 2017-10-09 08:30:00+00:00, Price: 7.4275
Timestamp: 2017-10-09 08:45:00+00:00, Price: 7.4192
Timestamp: 2017-10-09 09:00:00+00:00, Price: 7.4275
Timestamp: 2017-10-09 09:15:00+00:00, Price: 7.4357


# box plot
def plot_box_plot(selected):
    # getting year and month info from df
    year = selected["year"].iloc[0]
    month = selected["month"].iloc[0]
    # looping over stocks
    for index in indexes_selected:
        # initializing the plot, title, x & y axis
        plt.figure(figsize=(30, 15))
        ax = sns.boxplot(data=selected, x='month', y=index)
        plt.title(str(index) + " " + f'Stock Price Boxplot for {year}/{month}', fontsize=30, fontweight='bold')
        plt.xlabel('Month', fontsize=25)
        plt.ylabel('Stock Price', fontsize=25)

        # identifying and marking both upper and lower outliers
        threshold = 1.5  # Adjust this threshold as needed
        upper_outliers = selected[selected[index] > (selected[index].quantile(0.75) + threshold * (selected[index].quantile(0.75) - selected[index].quantile(0.25)))]
        lower_outliers = selected[selected[index] < (selected[index].quantile(0.25) - threshold * (selected[index].quantile(0.75) - selected[index].quantile(0.25)))]
        
        # printing information about the outliers
        if not upper_outliers.empty:
            print('Upper Outliers for ' + str(index) + " in " + str(year) + "/" + str(month))
            for _, row in upper_outliers.iterrows():
                timestamp = row['timestamp']
                price = row[index]
                print(f'Timestamp: {timestamp}, Price: {price}')

        if not lower_outliers.empty:
            print('Lower Outliers for ' + str(index) + " in " + str(year) + "/" + str(month))
            for _, row in lower_outliers.iterrows():
                timestamp = row['timestamp']
                price = row[index]
                print(f'Timestamp: {timestamp}, Price: {price}')
                
        if lower_outliers.empty and upper_outliers.empty:
            print("NO OUTLIERS")

        plt.show()

# group by year, month
grouped = df.groupby(['year', 'month'])

# apply the plot_box_plot function to each group
grouped.apply(plot_box_plot)

Upper Outliers for AKBNK in 2017/1
Timestamp: 2017-01-30 10:45:00+00:00, Price: 6.5238
Timestamp: 2017-01-30 11:00:00+00:00, Price: 6.5478
Timestamp: 2017-01-30 11:15:00+00:00, Price: 6.572
Timestamp: 2017-01-30 11:30:00+00:00, Price: 6.5639
Timestamp: 2017-01-30 11:45:00+00:00, Price: 6.5559
Timestamp: 2017-01-30 12:00:00+00:00, Price: 6.572
Timestamp: 2017-01-30 12:15:00+00:00, Price: 6.5799
Timestamp: 2017-01-30 12:30:00+00:00, Price: 6.5799
Timestamp: 2017-01-30 12:45:00+00:00, Price: 6.572
Timestamp: 2017-01-30 13:00:00+00:00, Price: 6.596
Timestamp: 2017-01-30 13:15:00+00:00, Price: 6.612
Timestamp: 2017-01-30 13:30:00+00:00, Price: 6.604
Timestamp: 2017-01-30 13:45:00+00:00, Price: 6.6281
Timestamp: 2017-01-30 14:00:00+00:00, Price: 6.6441
Timestamp: 2017-01-30 14:15:00+00:00, Price: 6.6681
Timestamp: 2017-01-30 14:30:00+00:00, Price: 6.6842
Timestamp: 2017-01-30 14:45:00+00:00, Price: 6.6842
Timestamp: 2017-01-30 15:00:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 06:45:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 07:00:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 07:15:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 07:30:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 07:45:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:00:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:15:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:30:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 08:45:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 09:00:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 09:15:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 09:30:00+00:00, Price: 6.7082
Timestamp: 2017-01-31 09:45:00+00:00, Price: 6.7163
Timestamp: 2017-01-31 10:45:00+00:00, Price: 6.7002
Timestamp: 2017-01-31 11:00:00+00:00, Price: 6.7403
Timestamp: 2017-01-31 11:15:00+00:00, Price: 6.7563
Timestamp: 2017-01-31 11:30:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 11:45:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 12:00:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 12:15:00+00:00, Price: 6.7082
Timestamp: 2017-01-31 12:30:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 12:45:00+00:00, Price: 6.6842
Timestamp: 2017-01-31 13:00:00+00:00, Price: 6.6921
Timestamp: 2017-01-31 13:15:00+00:00, Price: 6.6761
Timestamp: 2017-01-31 13:30:00+00:00, Price: 6.6681
Timestamp: 2017-01-31 13:45:00+00:00, Price: 6.7322
Timestamp: 2017-01-31 14:00:00+00:00, Price: 6.7163
Timestamp: 2017-01-31 14:15:00+00:00, Price: 6.7643
Timestamp: 2017-01-31 14:30:00+00:00, Price: 6.7803
Timestamp: 2017-01-31 14:45:00+00:00, Price: 6.7563
Timestamp: 2017-01-31 15:00:00+00:00, Price: 6.7322

Upper Outliers for VAKBN in 2017/1
Timestamp: 2017-01-30 08:15:00+00:00, Price: 4.7469
Timestamp: 2017-01-30 08:30:00+00:00, Price: 4.7469
Timestamp: 2017-01-30 08:45:00+00:00, Price: 4.786
Timestamp: 2017-01-30 09:00:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 09:15:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 09:30:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 09:45:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 10:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 11:00:00+00:00, Price: 4.8055
Timestamp: 2017-01-30 11:15:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 11:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 11:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 12:00:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 12:15:00+00:00, Price: 4.8349
Timestamp: 2017-01-30 12:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-30 12:45:00+00:00, Price: 4.8153
Timestamp: 2017-01-30 13:00:00+00:00, Price: 4.8349
Timestamp: 2017-01-30 13:15:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 13:30:00+00:00, Price: 4.8447
Timestamp: 2017-01-30 13:45:00+00:00, Price: 4.8447
Timestamp: 2017-01-30 14:00:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 14:15:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 14:30:00+00:00, Price: 4.8545
Timestamp: 2017-01-30 14:45:00+00:00, Price: 4.8643
Timestamp: 2017-01-30 15:00:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 06:45:00+00:00, Price: 4.8741
Timestamp: 2017-01-31 07:00:00+00:00, Price: 4.8545
Timestamp: 2017-01-31 07:15:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 07:30:00+00:00, Price: 4.8545
Timestamp: 2017-01-31 07:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 08:00:00+00:00, Price: 4.7958
Timestamp: 2017-01-31 08:15:00+00:00, Price: 4.8153
Timestamp: 2017-01-31 08:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 08:45:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 09:00:00+00:00, Price: 4.8741
Timestamp: 2017-01-31 09:15:00+00:00, Price: 4.8741
Timestamp: 2017-01-31 09:30:00+00:00, Price: 4.8937
Timestamp: 2017-01-31 09:45:00+00:00, Price: 4.8838
Timestamp: 2017-01-31 10:45:00+00:00, Price: 4.8838
Timestamp: 2017-01-31 11:00:00+00:00, Price: 4.9034
Timestamp: 2017-01-31 11:15:00+00:00, Price: 4.8937
Timestamp: 2017-01-31 11:30:00+00:00, Price: 4.9034
Timestamp: 2017-01-31 11:45:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 12:00:00+00:00, Price: 4.8545
Timestamp: 2017-01-31 12:15:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 12:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 12:45:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 13:00:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 13:15:00+00:00, Price: 4.8153
Timestamp: 2017-01-31 13:30:00+00:00, Price: 4.8252
Timestamp: 2017-01-31 13:45:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 14:00:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 14:15:00+00:00, Price: 4.8643
Timestamp: 2017-01-31 14:30:00+00:00, Price: 4.8349
Timestamp: 2017-01-31 14:45:00+00:00, Price: 4.8153
Timestamp: 2017-01-31 15:00:00+00:00, Price: 4.8055
Lower Outliers for VAKBN in 2017/1
Timestamp: 2017-01-16 06:45:00+00:00, Price: 4.4434
Timestamp: 2017-01-16 14:45:00+00:00, Price: 4.4337
Timestamp: 2017-01-16 15:00:00+00:00, Price: 4.4238
Timestamp: 2017-01-17 07:00:00+00:00, Price: 4.4337
Timestamp: 2017-01-17 08:45:00+00:00, Price: 4.4434

NO OUTLIERS

NO OUTLIERS

NO OUTLIERS

NO OUTLIERS

Lower Outliers for AKBNK in 2017/2
Timestamp: 2017-02-01 06:45:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 07:00:00+00:00, Price: 6.7403
Timestamp: 2017-02-01 07:15:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 07:30:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 07:45:00+00:00, Price: 6.6842
Timestamp: 2017-02-01 08:00:00+00:00, Price: 6.7082
Timestamp: 2017-02-01 08:15:00+00:00, Price: 6.6681
Timestamp: 2017-02-01 08:30:00+00:00, Price: 6.6521
Timestamp: 2017-02-01 08:45:00+00:00, Price: 6.6842
Timestamp: 2017-02-01 09:00:00+00:00, Price: 6.7082
Timestamp: 2017-02-01 09:15:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 09:30:00+00:00, Price: 6.7082
Timestamp: 2017-02-01 09:45:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 10:00:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 10:45:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 11:00:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 11:15:00+00:00, Price: 6.7163
Timestamp: 2017-02-01 11:30:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 11:45:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 12:00:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 12:15:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 12:30:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 12:45:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 13:00:00+00:00, Price: 6.7322
Timestamp: 2017-02-01 13:15:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 13:30:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 13:45:00+00:00, Price: 6.7242
Timestamp: 2017-02-01 14:00:00+00:00, Price: 6.7483
Timestamp: 2017-02-01 14:30:00+00:00, Price: 6.7643
Timestamp: 2017-02-01 14:45:00+00:00, Price: 6.7643
Timestamp: 2017-02-01 15:00:00+00:00, Price: 6.7724

Lower Outliers for VAKBN in 2017/2
Timestamp: 2017-02-01 06:45:00+00:00, Price: 4.8252
Timestamp: 2017-02-01 07:00:00+00:00, Price: 4.8545
Timestamp: 2017-02-01 07:15:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 07:30:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 07:45:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:00:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 08:15:00+00:00, Price: 4.8153
Timestamp: 2017-02-01 08:30:00+00:00, Price: 4.8055
Timestamp: 2017-02-01 08:45:00+00:00, Price: 4.8349
Timestamp: 2017-02-01 09:00:00+00:00, Price: 4.8447
Timestamp: 2017-02-01 09:15:00+00:00, Price: 4.8447
Timestamp: 2017-02-01 09:30:00+00:00, Price: 4.8447

Upper Outliers for ARCLK in 2017/2
Timestamp: 2017-02-01 08:00:00+00:00, Price: 21.9405
Timestamp: 2017-02-01 08:15:00+00:00, Price: 21.9029
Timestamp: 2017-02-01 08:30:00+00:00, Price: 22.1282
Timestamp: 2017-02-01 08:45:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 09:00:00+00:00, Price: 21.978
Timestamp: 2017-02-01 09:15:00+00:00, Price: 21.978
Timestamp: 2017-02-01 09:30:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 09:45:00+00:00, Price: 22.1094
Timestamp: 2017-02-01 10:00:00+00:00, Price: 22.053150000000002
Timestamp: 2017-02-01 10:45:00+00:00, Price: 21.9969
Timestamp: 2017-02-01 11:00:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 11:15:00+00:00, Price: 22.0719
Timestamp: 2017-02-01 11:30:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 11:45:00+00:00, Price: 22.0907
Timestamp: 2017-02-01 12:00:00+00:00, Price: 22.1282
Timestamp: 2017-02-01 12:15:00+00:00, Price: 22.0531
Timestamp: 2017-02-01 12:30:00+00:00, Price: 22.0156
Timestamp: 2017-02-01 12:45:00+00:00, Price: 22.0156
Timestamp: 2017-02-01 13:00:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 13:15:00+00:00, Price: 21.978
Timestamp: 2017-02-01 13:30:00+00:00, Price: 21.9969
Timestamp: 2017-02-01 13:45:00+00:00, Price: 22.0344
Timestamp: 2017-02-01 14:00:00+00:00, Price: 21.9593
Timestamp: 2017-02-01 14:15:00+00:00, Price: 21.978
Timestamp: 2017-02-01 14:30:00+00:00, Price: 21.978
Timestamp: 2017-02-01 14:45:00+00:00, Price: 22.0156
Timestamp: 2017-02-01 15:00:00+00:00, Price: 22.1094
Timestamp: 2017-02-02 06:45:00+00:00, Price: 22.1282
Timestamp: 2017-02-02 07:00:00+00:00, Price: 22.0531
Timestamp: 2017-02-02 07:15:00+00:00, Price: 22.3159
Timestamp: 2017-02-02 07:30:00+00:00, Price: 22.466
Timestamp: 2017-02-02 07:45:00+00:00, Price: 22.4472
Timestamp: 2017-02-02 08:00:00+00:00, Price: 22.0719
Timestamp: 2017-02-02 08:15:00+00:00, Price: 22.0907
Timestamp: 2017-02-02 08:30:00+00:00, Price: 22.0344
Timestamp: 2017-02-02 08:45:00+00:00, Price: 22.0344
Timestamp: 2017-02-02 09:00:00+00:00, Price: 22.0156
Timestamp: 2017-02-02 09:15:00+00:00, Price: 21.9593
Timestamp: 2017-02-02 09:30:00+00:00, Price: 21.9969
Timestamp: 2017-02-02 09:45:00+00:00, Price: 21.9593
Timestamp: 2017-02-02 10:45:00+00:00, Price: 21.9405
Timestamp: 2017-02-03 06:45:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 07:00:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 07:15:00+00:00, Price: 22.4285
Timestamp: 2017-02-03 07:30:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 07:45:00+00:00, Price: 22.2783
Timestamp: 2017-02-03 08:00:00+00:00, Price: 22.2783
Timestamp: 2017-02-03 08:15:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 08:30:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 08:45:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 09:00:00+00:00, Price: 22.2596
Timestamp: 2017-02-03 09:15:00+00:00, Price: 22.2783
Timestamp: 2017-02-03 09:30:00+00:00, Price: 22.2971
Timestamp: 2017-02-03 09:45:00+00:00, Price: 22.2596
Timestamp: 2017-02-03 10:45:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 11:00:00+00:00, Price: 22.4098
Timestamp: 2017-02-03 11:15:00+00:00, Price: 22.3534
Timestamp: 2017-02-03 11:30:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 11:45:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 12:00:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 12:15:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 12:30:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 12:45:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 13:00:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 13:15:00+00:00, Price: 22.2971
Timestamp: 2017-02-03 13:30:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 13:45:00+00:00, Price: 22.3159
Timestamp: 2017-02-03 14:00:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 14:15:00+00:00, Price: 22.3722
Timestamp: 2017-02-03 14:30:00+00:00, Price: 22.3347
Timestamp: 2017-02-03 14:45:00+00:00, Price: 22.2408
Timestamp: 2017-02-03 15:00:00+00:00, Price: 22.3347
Timestamp: 2017-02-06 06:45:00+00:00, Price: 22.5223
Timestamp: 2017-02-06 07:00:00+00:00, Price: 22.5599
Timestamp: 2017-02-06 07:15:00+00:00, Price: 22.5411
Timestamp: 2017-02-06 07:30:00+00:00, Price: 22.6161
Timestamp: 2017-02-06 07:45:00+00:00, Price: 22.4848
Timestamp: 2017-02-06 08:00:00+00:00, Price: 22.3159
Timestamp: 2017-02-06 08:15:00+00:00, Price: 22.2408
Timestamp: 2017-02-06 08:30:00+00:00, Price: 22.1845
Timestamp: 2017-02-06 08:45:00+00:00, Price: 22.1658
Timestamp: 2017-02-06 09:00:00+00:00, Price: 22.1845
Timestamp: 2017-02-06 09:15:00+00:00, Price: 22.2033
Timestamp: 2017-02-06 09:30:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 09:45:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 10:45:00+00:00, Price: 22.1094
Timestamp: 2017-02-06 11:00:00+00:00, Price: 22.1845
Timestamp: 2017-02-06 11:15:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 11:30:00+00:00, Price: 22.1282
Timestamp: 2017-02-06 11:45:00+00:00, Price: 22.1658
Timestamp: 2017-02-06 12:00:00+00:00, Price: 22.222
Timestamp: 2017-02-06 12:15:00+00:00, Price: 22.2033
Timestamp: 2017-02-06 12:30:00+00:00, Price: 22.2033
Timestamp: 2017-02-06 12:45:00+00:00, Price: 22.2783
Timestamp: 2017-02-06 13:00:00+00:00, Price: 22.2971
Timestamp: 2017-02-06 13:15:00+00:00, Price: 22.2596
Timestamp: 2017-02-06 13:30:00+00:00, Price: 22.1469
Timestamp: 2017-02-06 13:45:00+00:00, Price: 22.0719
Timestamp: 2017-02-06 14:00:00+00:00, Price: 21.9969
Timestamp: 2017-02-06 14:15:00+00:00, Price: 22.0344
Timestamp: 2017-02-06 14:30:00+00:00, Price: 22.0719
Timestamp: 2017-02-06 14:45:00+00:00, Price: 22.0531
Timestamp: 2017-02-06 15:00:00+00:00, Price: 22.0531

Upper Outliers for TUPRS in 2017/2
Timestamp: 2017-02-14 06:45:00+00:00, Price: 64.8258


# returns outliers obtained from control charts in part 3
def get_outliers(stock_name):
    if stock_name == "AKBNK":
        return outliers_akbnk
    elif stock_name == "VAKBN":
        return outliers_vakbn
    elif stock_name == "ARCLK":
        return outliers_arclk
    elif stock_name == "TUPRS":
        return outliers_tuprs
    elif stock_name == "TCELL":
        return outliers_tcell
    else:
        return outliers_thyao

'''
Below function plots the quarterly trend, stock prices, and outliers 
given the stock name.
'''
def plot_trend(stock_name):
    df_trend = pd.read_csv(f"{stock_name}.csv", header=0)
    df_trend["Week"] = pd.to_datetime(df_trend["Week"])
    
    # start and end dates
    # notice that we observe them quarterly
    # but the outlier data we used is obtained from part 3, therefore monthly
    start = pd.to_datetime("2017-01-15")
    end = start + pd.DateOffset(months=3)
    
    # initialize the figure
    fig, axes = plt.subplots(4, 2, figsize=(22, 22))
    fig.subplots_adjust(hspace=0.5)
    
    # we have 8 quarters (2 years)
    for i in range(8):
        # get the outliers for quarter
        outliers = get_outliers(stock_name)
        outliers_quarter = [i for i in outliers if i[0] >= start.tz_localize('UTC') and i[0] <= end.tz_localize('UTC')]
        outliers_quarter = pd.DataFrame(outliers_quarter, columns=['timestamp', 'value'])
        # copy df
        df = filled_df.copy()
        # filtering with time and adding UTC timezone
        ndf = df[(df['timestamp'] >= start.tz_localize('UTC')) & (df['timestamp'] <= end.tz_localize('UTC'))]
        # filtering the necessary columns 
        df = ndf[['timestamp', 'year', 'month', 'day', stock_name]].copy()
        
        # We must scale both of the df between 0-1 since their scale is very different
        scaler = MinMaxScaler(feature_range=(0, 1))
        df[stock_name] = scaler.fit_transform(df[stock_name].values.reshape(-1, 1))
        # scale outliers
        if len(outliers_quarter) > 0:
            outliers_quarter["value"] = scaler.transform(outliers_quarter["value"].values.reshape(-1, 1))
            
        # filtering df_akbnk with the same timezone
        df_trend_filtered = df_trend[(df_trend['Week'] >= start) & (df_trend['Week'] <= end)]

        # min max scaler 
        scaler = MinMaxScaler(feature_range=(0, 1))
        df_trend_filtered[f"IST:{stock_name}: (Türkiye)"] = scaler.fit_transform(df_trend_filtered[f"IST:{stock_name}: (Türkiye)"].values.reshape(-1, 1))

        row = i // 2
        col = i % 2

        # creating a figure and axis
        ax = axes[row, col]
        # plotting the data from df_akbnk_filtered
        ax.plot(df_trend_filtered["Week"], df_trend_filtered[f"IST:{stock_name}: (Türkiye)"], label="Trend Data", color="green", linewidth=3)

        # plotting the data from df_copy (assuming "timestamp" is your x-axis)
        ax.plot(df["timestamp"], df[stock_name], label="Stock Price", color="orangered")

        # plot outliers as red dots
        if len(outliers_quarter) > 0:
            ax.scatter(outliers_quarter['timestamp'], outliers_quarter['value'], c='red', marker='o', s=100, label='Outliers')

        # x, y axis, title and legend
        ax.set_xlabel(f"Q{i % 4 + 1} {start.year}", fontsize=16)
        ax.set_ylabel("Stock Prices and \nSearch Volume \n(Normalized 0-1)", fontsize=16)
        ax.grid(True)
        ax.set_title(f"{stock_name}\nTrend Data vs. Stock Price", fontsize=18, fontweight="bold")
        ax.legend(loc="upper left")

        # off-setting dates
        start = end
        end = start + relativedelta(months=3)

    plt.show()


plot_trend("AKBNK")


plot_trend("VAKBN")


plot_trend("ARCLK")


plot_trend("TUPRS")


plot_trend("TCELL")


plot_trend("THYAO")

	timestamp	AEFES	AKBNK	AKSA	AKSEN	ALARK	ALBRK	ANACM	ARCLK	ASELS	...	USAK	VAKBN	VESTL	YATAS	YKBNK	YUNSA	ZOREN	year	month	day
0	2012-09-17 06:45:00+00:00	22.3978	5.2084	1.7102	3.87	1.4683	1.1356	1.0634	6.9909	2.9948	...	1.0382	3.8620	1.90	0.4172	2.5438	2.2619	0.7789	2012	9	17
1	2012-09-17 07:00:00+00:00	22.3978	5.1938	1.7066	3.86	1.4574	1.1275	1.0634	6.9259	2.9948	...	1.0382	3.8529	1.90	0.4229	2.5266	2.2462	0.7789	2012	9	17
2	2012-09-17 07:15:00+00:00	22.3978	5.2084	1.7102	NaN	1.4610	1.1356	1.0679	6.9909	2.9855	...	1.0463	3.8436	1.91	0.4229	2.5266	2.2566	0.7789	2012	9	17
3	2012-09-17 07:30:00+00:00	22.3978	5.1938	1.7102	3.86	1.4537	1.1275	1.0679	6.9584	2.9855	...	1.0382	3.8529	1.91	0.4286	2.5324	2.2619	0.7860	2012	9	17
4	2012-09-17 07:45:00+00:00	22.5649	5.2084	1.7102	3.87	1.4574	1.1356	1.0725	6.9909	2.9760	...	1.0382	3.8620	1.90	0.4286	2.5324	2.2619	0.7789	2012	9	17
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
50007	2019-07-23 14:00:00+00:00	20.4800	7.7300	9.1400	2.47	3.2300	1.2100	2.8400	20.3000	NaN	...	1.0500	4.8600	9.98	5.3500	2.7500	4.2500	NaN	2019	7	23
50008	2019-07-23 14:15:00+00:00	20.5000	7.7200	9.1400	2.47	3.2200	1.2100	2.8400	20.3200	NaN	...	1.0500	4.8600	9.98	5.3400	2.7500	4.2400	NaN	2019	7	23
50009	2019-07-23 14:30:00+00:00	20.5000	7.7400	9.1300	2.46	3.2300	1.2100	2.8300	20.3400	NaN	...	1.0500	4.8600	9.96	5.3400	2.7600	4.2400	NaN	2019	7	23
50010	2019-07-23 14:45:00+00:00	20.4000	7.7000	9.1400	2.47	3.2400	1.2100	2.8200	20.3800	NaN	...	1.0400	4.8600	9.94	5.3400	2.7700	4.2400	NaN	2019	7	23
50011	2019-07-23 15:00:00+00:00	20.4600	7.7000	9.1400	2.47	3.2300	1.2000	2.8300	20.3200	NaN	...	1.0500	4.8500	9.93	5.3300	2.7700	4.2400	NaN	2019	7	23

	timestamp	AEFES	AKBNK	AKSA	AKSEN	ALARK	ALBRK	ANACM	ARCLK	ASELS	...	USAK	VAKBN	VESTL	YATAS	YKBNK	YUNSA	ZOREN	year	month	day
0	2012-09-17 06:45:00+00:00	22.3978	5.2084	1.7102	3.87	1.4683	1.1356	1.0634	6.9909	2.9948	...	1.0382	3.8620	1.90	0.4172	2.5438	2.2619	0.7789	2012	9	17
1	2012-09-17 07:00:00+00:00	22.3978	5.1938	1.7066	3.86	1.4574	1.1275	1.0634	6.9259	2.9948	...	1.0382	3.8529	1.90	0.4229	2.5266	2.2462	0.7789	2012	9	17
2	2012-09-17 07:15:00+00:00	22.3978	5.2084	1.7102	3.86	1.4610	1.1356	1.0679	6.9909	2.9855	...	1.0463	3.8436	1.91	0.4229	2.5266	2.2566	0.7789	2012	9	17
3	2012-09-17 07:30:00+00:00	22.3978	5.1938	1.7102	3.86	1.4537	1.1275	1.0679	6.9584	2.9855	...	1.0382	3.8529	1.91	0.4286	2.5324	2.2619	0.7860	2012	9	17
4	2012-09-17 07:45:00+00:00	22.5649	5.2084	1.7102	3.87	1.4574	1.1356	1.0725	6.9909	2.9760	...	1.0382	3.8620	1.90	0.4286	2.5324	2.2619	0.7789	2012	9	17
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
50007	2019-07-23 14:00:00+00:00	20.4800	7.7300	9.1400	2.47	3.2300	1.2100	2.8400	20.3000	NaN	...	1.0500	4.8600	9.98	5.3500	2.7500	4.2500	NaN	2019	7	23
50008	2019-07-23 14:15:00+00:00	20.5000	7.7200	9.1400	2.47	3.2200	1.2100	2.8400	20.3200	NaN	...	1.0500	4.8600	9.98	5.3400	2.7500	4.2400	NaN	2019	7	23
50009	2019-07-23 14:30:00+00:00	20.5000	7.7400	9.1300	2.46	3.2300	1.2100	2.8300	20.3400	NaN	...	1.0500	4.8600	9.96	5.3400	2.7600	4.2400	NaN	2019	7	23
50010	2019-07-23 14:45:00+00:00	20.4000	7.7000	9.1400	2.47	3.2400	1.2100	2.8200	20.3800	NaN	...	1.0400	4.8600	9.94	5.3400	2.7700	4.2400	NaN	2019	7	23
50011	2019-07-23 15:00:00+00:00	20.4600	7.7000	9.1400	2.47	3.2300	1.2000	2.8300	20.3200	NaN	...	1.0500	4.8500	9.93	5.3300	2.7700	4.2400	NaN	2019	7	23

1. Importing the necessary libraries for analysis and visualizations¶

2. Reading the data and preprocessing¶

3. Analysis¶

3.1. Control Charts¶

3.2. Box Plots¶

4. Google Trends¶

4.1 IST:AKBNK¶

4.2 IST:VAKBN¶

4.3 IST:ARCLK¶

4.4 IST:TUPRS¶

4.5 IST:TCELL¶

4.6 IST:THYAO¶